Statistical Model in Predicting Traffic Congestion Among Selected Routes in Metro Manila

Abstract

Traffic congestion is a serious issue that contributes significantly to economic loss, increase in greenhouse gas emissions, and fuel wastage. Hence, an accurate congestion prediction model can help address these problems. This paper analyzes the status of the road transport infrastructure, public transportation system, volume of vehicles, road crash data, and government policies, rules, and regulations, as well as the quality of implementation. Moreover, traffic congestion prediction models were developed, using logistic regression, random forest, and neural networks. Seventeen months of daily traffic data were used in developing the models. Results showed that the Random Forest models have recorded the highest accuracy (77%), recall (77%) and F1-score (77%). On the other hand, the Neural Network model has better performance in predicting Free Flow traffic congestion at 81% F1-score, while the Random Forest model showed better results in predicting Moderate, Heavy, and Standstill Traffic.